Intelligent Vision Based Technique Using ANN for Surface Finish Assessment of Machined Components

Abstract:

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In this work, an FPGA hardware based image processing algorithm for preprocessing the
images and enhance the image quality has been developed. The captured images were processed
using a FPGA chip to remove the noise and then using a neural network, the surface roughness of
machined parts produced by the grinding process was estimated. To ensure the effectiveness of this
approach the roughness values quantified using these image vision techniques were then compared
with widely accepted standard mechanical stylus instrument values. Quantification of digital images
for surface roughness was performed by extracting key image features using Fourier transform and
the standard deviation of gray level intensity values. A VLSI chip belonging to the Xilinx family
Spartan-IIE FPGA board was used for the hardware based filter implementation. The coding was
done using the popular VHDL language with the algorithms developed so as to exploit the implicit
parallel processing capability of the chip. Thus, in this work an exhaustive analysis was done with
comparison studies wherever required to make sure that the present approach of estimating surface
finish based on the computer vision processing of image is more accurate and could be
implemented in real time on a chip.

Abstract: Hard turning has the advantage of rapidly, elasticity and low energy consuming. It has
been a trend to replace the complex grinding processes, especially for small batch machining.The
surface roughness value of steel after being grinded will ranged in 0.1 to 1.6 μm Ra. This paper
points to the precision hard turning of the hardened mold steel, seeking the cutting conditions that
can be received in the surface roughness value below 0.1μm Ra, in order to replace the grinding
processes.
The precision dry turning test were conducted with ceramic cutting tools. The nose radius of the
cutting tool was 1.2 mm and the depth of cut was fixed at 0.05 mm. Through a series of turning test,
it can be found that, when cutting speed was at 80 to 200 m / min, and feed rate at 0.005 to 0.009
mm / rev, the surface roughness value would be all below 0.1μm Ra. It was superior to grinding
process. So we can say that, it is possible to replace the grinding process by hard turning when
machining the hardened mold steel.

Abstract: In this paper, Taguchi method was applied to design the cutting experiments when end milling Inconel 718 with the TiAlN-TiN coated carbide inserts. The signal-to-noise (S/N) ratio are employed to study the effects of cutting parameters (cutting speed, feed per tooth, radial depth of cut, and axial depth of cut) on surface roughness, and the optimal combination of the cutting parameters for the desired surface roughness is obtained. An exponential regression model for the surface roughness is formulated based on the experimental results. Finally, the verification tests show that surface roughness generated by the optimal cutting parameters is really the minimum value, and there is a good agreement between the predictive results and experimental measurements.

Abstract: The surface roughness is difficult to estimate in machining, especially for weak stiffness workpiece. So, prediction model of surface roughness using artificial neural network (ANN) is developed. This model investigates the effects of cutting parameters during milling Aluminum alloy 6061. The experiments are planned with four factors and four levels for developing the knowledge base for ANN training. Three-dimensional surface plots are generated using ANN model to study the effects of cutting parameters on surface roughness. The analysis reveals that cutting speed and feed rate have significant effects in reducing the surface roughness, while the axial and radial depth of cut has less effect. And the variations of surface roughness are highly non-linear with the cutting parameters.

Abstract: Accurate predictive modelling is an essential prerequisite for optimization and control of production in modern manufacturing environments. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the surface roughness in high speed turning of AISI P 20 tool steel. Experiments were designed and performed to collect the training and testing data for the proposed model based on orthogonal array. For decreasing the complexity of the ANFIS structure, principal component analysis (PCA) was used to deal with the experimental data. The comparison between predictions and experimental data showed that the proposed method was both effective and efficient for modelling surface roughness.

Abstract: Based on the engine lathe, the vibratory turning experiment system was built up with low frequency of the mechanical vibratory device. Orthogonal experiment was carried out for the findings of the chip configuration, chip breakage, and the surface roughness with relationship of the cutting engagement especially the combination of the vibratory frequency, amplitude, and the feed and cutting velocity. Regressive analysis was done for the empirical formula of the roughness with parameters of vibratory turning engagement. From the experiment it has been shown that the higher the frequency is in vibratory turning, the smoother the machined surface is, other conditions being equal. The surface roughness increases with the feed, at the same frequency. The cutting tool was still smooth on the rake and flank and along the cutting edges all the time in vibratory turning. Because of the vibration and the lubrication the BUE can hardly be built up, being good for the surface integrity.